NBDT: Neural-Backed Decision Trees

• URL: http://arxiv.org/abs/2004.00221v3
• Date: Thu, 28 Jan 2021 03:06:26 GMT
• Title: NBDT: Neural-Backed Decision Trees
• Authors: Alvin Wan, Lisa Dunlap, Daniel Ho, Jihan Yin, Scott Lee, Henry Jin, Suzanne Petryk, Sarah Adel Bargal, Joseph E. Gonzalez
• Abstract summary: We improve accuracy and interpretability using Neural-Backed Decision Trees (NBDTs) NBDTs replace a neural network's final linear layer with a differentiable sequence of decisions and a surrogate loss. Our surrogate loss improves the original model's accuracy by up to 2%.
• Score: 26.2115887956431
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use. In response, previous work combines decision trees with deep learning, yielding models that (1) sacrifice interpretability for accuracy or (2) sacrifice accuracy for interpretability. We forgo this dilemma by jointly improving accuracy and interpretability using Neural-Backed Decision Trees (NBDTs). NBDTs replace a neural network's final linear layer with a differentiable sequence of decisions and a surrogate loss. This forces the model to learn high-level concepts and lessens reliance on highly-uncertain decisions, yielding (1) accuracy: NBDTs match or outperform modern neural networks on CIFAR, ImageNet and better generalize to unseen classes by up to 16%. Furthermore, our surrogate loss improves the original model's accuracy by up to 2%. NBDTs also afford (2) interpretability: improving human trustby clearly identifying model mistakes and assisting in dataset debugging. Code and pretrained NBDTs are at https://github.com/alvinwan/neural-backed-decision-trees.

Related papers

• BiDense: Binarization for Dense Prediction [62.70804353158387]
  BiDense is a generalized binary neural network (BNN) designed for efficient and accurate dense prediction tasks. BiDense incorporates two key techniques: the Distribution-adaptive Binarizer (DAB) and the Channel-adaptive Full-precision Bypass (CFB)
  arXiv  Detail & Related papers  (2024-11-15T16:46:04Z)
• An Interpretable Power System Transient Stability Assessment Method with Expert Guiding Neural-Regression-Tree [12.964139269555277]
  An interpretable power system Transient Stability Assessment method with Expert guiding Neural-Regression-Tree (TSA-ENRT) is proposed. TSA-ENRT utilizes an expert guiding nonlinear regression tree to approximate the neural network prediction and the neural network can be explained by the interpretive rules generated by the tree model. Extensive experiments indicate the interpretive rules generated by the proposed TSA-ENRT are highly consistent with the neural network prediction and more agreed with human expert cognition.
  arXiv  Detail & Related papers  (2024-04-03T08:22:41Z)
• Human Trajectory Forecasting with Explainable Behavioral Uncertainty [63.62824628085961]
  Human trajectory forecasting helps to understand and predict human behaviors, enabling applications from social robots to self-driving cars. Model-free methods offer superior prediction accuracy but lack explainability, while model-based methods provide explainability but cannot predict well. We show that BNSP-SFM achieves up to a 50% improvement in prediction accuracy, compared with 11 state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-07-04T16:45:21Z)
• NCTV: Neural Clamping Toolkit and Visualization for Neural Network Calibration [66.22668336495175]
  A lack of consideration for neural network calibration will not gain trust from humans. We introduce the Neural Clamping Toolkit, the first open-source framework designed to help developers employ state-of-the-art model-agnostic calibrated models.
  arXiv  Detail & Related papers  (2022-11-29T15:03:05Z)
• Scaling Laws Beyond Backpropagation [64.0476282000118]
  We study the ability of Direct Feedback Alignment to train causal decoder-only Transformers efficiently. We find that DFA fails to offer more efficient scaling than backpropagation.
  arXiv  Detail & Related papers  (2022-10-26T10:09:14Z)
• Can pruning improve certified robustness of neural networks? [106.03070538582222]
  We show that neural network pruning can improve empirical robustness of deep neural networks (NNs) Our experiments show that by appropriately pruning an NN, its certified accuracy can be boosted up to 8.2% under standard training. We additionally observe the existence of certified lottery tickets that can match both standard and certified robust accuracies of the original dense models.
  arXiv  Detail & Related papers  (2022-06-15T05:48:51Z)
• S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration [74.5509794733707]
  We present a novel guided learning paradigm from real-valued to distill binary networks on the final prediction distribution. Our proposed method can boost the simple contrastive learning baseline by an absolute gain of 5.515% on BNNs. Our method achieves substantial improvement over the simple contrastive learning baseline, and is even comparable to many mainstream supervised BNN methods.
  arXiv  Detail & Related papers  (2021-02-17T18:59:28Z)
• Improving Accuracy of Binary Neural Networks using Unbalanced Activation Distribution [12.46127622357824]
  We show that unbalanced activation distribution can actually improve the accuracy of BNNs. We also show that adjusting the threshold values of binary activation functions results in the unbalanced distribution of the binary activation. Experimental results show that the accuracy of previous BNN models can be improved by simply shifting the threshold values of binary activation functions.
  arXiv  Detail & Related papers  (2020-12-02T02:49:53Z)
• On Resource-Efficient Bayesian Network Classifiers and Deep Neural Networks [14.540226579203207]
  We present two methods to reduce the complexity of Bayesian network (BN) classifiers. First, we introduce quantization-aware training using the straight-through gradient estimator to quantize the parameters of BNs to few bits. Second, we extend a recently proposed differentiable tree-augmented naive Bayes (TAN) structure learning approach by also considering the model size.
  arXiv  Detail & Related papers  (2020-10-22T14:47:55Z)
• SegNBDT: Visual Decision Rules for Segmentation [26.90558353725608]
  We build a hybrid neural-network and decision-tree model for segmentation. We obtain semantic visual meaning by extending saliency methods to segmentation. Our model attains accuracy within 2-4% of the state-of-the-art HRNetV2 segmentation model.
  arXiv  Detail & Related papers  (2020-06-11T23:10:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.